Scientific discovery increasingly entails long-horizon exploration of complex hypothesis spaces, yet most existing approaches emphasize final performance while offering limited insight into how scientific exploration unfolds over time, particularly balancing efficiency-diversity trade-offs and supporting reproducible, human-in-the-loop discovery workflows. We introduce SelfAI, a self-directed, multi-agent-enabled discovery system that automates scientific exploration as a strategic, trajectory-driven decision-making process. SelfAI translates high-level research intent into executable experiments, reasons over accumulated experimental trajectories to guide subsequent exploration, and applies adaptive stopping decisions to terminate unproductive search paths within a closed-loop workflow governed by explicit efficiency-diversity trade-offs. Evaluated using real-world experiments spanning domains from machine learning to drug discovery, SelfAI consistently discovers high-quality solutions with substantially fewer redundant trials than classical optimization and recent LLM-based baselines. The proposed methods establish a general framework for organizing long-horizon scientific discovery and adaptive decision-making in complex scientific and engineering systems.

翻译：科学发现日益涉及对复杂假设空间的长周期探索，然而现有方法大多强调最终性能，对科学探索随时间如何展开——特别是效率与多样性权衡的平衡，以及如何支持可重现、人机协同的发现工作流——提供的洞察有限。本文提出SelfAI，一种自主引导、多智能体赋能的发现系统，它将科学探索自动化地组织为一个战略性、轨迹驱动的决策过程。SelfAI将高层研究意图转化为可执行的实验，基于累积的实验轨迹进行推理以指导后续探索，并在由显式效率-多样性权衡控制的闭环工作流中应用自适应停止决策以终止无效搜索路径。通过在机器学习至药物发现等多个领域的真实实验进行评估，SelfAI相比经典优化方法和近期基于大语言模型的基线方法，能够以显著更少的冗余试验持续发现高质量解决方案。所提出的方法为组织复杂科学与工程系统中的长周期科学发现与自适应决策建立了一个通用框架。